This work presents a new and efficient approach to discriminative speaker verification in the i-vector space. We illustrate the development of a linear discriminative classifier that is trained to discriminate between the hypothesis that a pair of feature vectors in a trial belong to the same speaker or to different speakers. This approach is alternative to the usual discriminative setup that discriminates between a speaker and all the other speakers. We use a discriminative classifier based on a Support Vector Machine (SVM) that is trained to estimate the parameters of a symmetric quadratic function approximating a log-likelihood ratio score without explicit modeling of the i-vector distributions as in the generative Probabilistic Linear Discriminant Analysis (PLDA) models. Training these models is feasible because it is not necessary to expand the i -vector pairs, which would be expensive or even impossible even for medium sized training sets. The results of experiments performed on the tel-tel extended core condition of the NIST 2010 Speaker Recognition Evaluation are competitive with the ones obtained by generative models, in terms of normalized Detection Cost Function and Equal Error Rate. Moreover, we show that it is possible to train a gender-independent discriminative model that achieves state-of-the-art accuracy, comparable to the one of a gender-dependent system, saving memory and execution time both in training and in testing.

The i-vector extraction process is affected by several factors such as the noise level, the acoustic content of the observed features, the channel mismatch between the training conditions and the test data, and the duration of the analyzed speech segment. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance. This paper presents a new PLDA model that, unlike the standard one, exploits the intrinsic i-vector uncertainty. Since the recognition accuracy is known to decrease for short speech segments, and their length is one of the main factors affecting the i-vector covariance, we designed a set of experiments aiming at comparing the standard and the new PLDA models on short speech cuts of variable duration, randomly extracted from the conversations included in the NIST SRE 2010 extended dataset, both from interviews and telephone conversations. Our results on NIST SRE 2010 evaluation data show that in different conditions the new model outperforms the standard PLDA by more than 10% relative when tested on short segments with duration mismatches, and is able to keep the accuracy of the standard model for long enough speaker segments. This technique has also been successfully tested in the NIST SRE 2012 evaluation.

This paper proposes to estimate parametric nonlinear transformations of i–vectors for speaker recognition systems based on Probabilistic Linear Discriminant Analysis (PLDA) classification. The Gaussian PLDA model assumes that the i-vectors are distributed according to the standard normal distribution. However it has been shown that the i–vectors are better modeled, for example, by Heavy–Tailed distributions, and that significant improvement of the classification performance can be obtained by whitening and length normalizing the i-vectors. In this work we propose to transform the i–vectors so that their distribution becomes more suitable to discriminate speakers using the PLDA model. This is performed by means of a sequence of affine and non–linear transformations whose parameters are obtained by Maximum Likelihood (ML) estimation on the development set. Another contribution of this work is the reduction of the mismatch between the development and evaluation i–vector length distributions by means of a scaling factor tuned for the estimated i–vector distribution, rather than by means of a blind length normalization. Relative improvement between 7% and 14% of the Detection Cost Function was obtained with the proposed technique on the NIST SRE-2010 and SRE-2012 evaluation datasets, using both the traditional GMM/UBM and the hybrid DNN/GMM based systems.

Systems based on i—vectors represent the current state-of-the-art in text-independent speaker recognition. Unlike Joint Factor Analysis (JFA), which models both speaker and intersession subspaces separately, in the i-vector approach all the important variability is modeled in a single low dimensional subspace. This work is based on the observation that JFA estimates a more informative speaker subspace than the “total variability” i-vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose a speaker modeling approach that extracts a compact representation of a speech segment, similar to the speaker factors of JFA and to i-vectors, referred to as “e-vector”. Estimating the e-vector subspace follows a procedure similar to i-vector training, but produces a more accurate speaker subspace, as confirmed by the results of a set of tests performed on the NIST 2012 and 2010 Speaker Recognition Evaluations. Simply replacing the i-vectors with e-vectors we get approximately 10% average improvement of the Cprimary cost function, using different systems and classifiers. It is worth noting that these performance gains come without any additional memory or computational costs with respect to the standard i-vector systems.

The evaluation of spoken language understanding (SLU) systems is often restricted to assessing their global performance or examining predefined subgroups of interest. However, a more detailed analysis at the subgroup level has the potential to uncover valuable insights into how speech system performance differs across various subgroups. In this work, we identify biased data subgroups and describe them at the level of user demographics, recording conditions, and speech targets. We propose a new task-, model- and dataset-agnostic approach to detect significant intra- and cross-model performance gaps. We detect problematic data subgroups in SLU models by leveraging the notion of subgroup divergence. We also compare the outcome of different SLU models on the same dataset and task at the subgroup level. We identify significant gaps in subgroup performance between models different in size, architecture, or pre-training objectives, including multi-lingual and mono-lingual models, yet comparable to each other in overall performance. The results, obtained on two SLU models, four datasets, and three different tasks–intent classification, automatic speech recognition, and emotion recognition–confirm the effectiveness of the proposed approach in providing a nuanced SLU model assessment.